Addendum to: Heterogeneity Coefficients for Mahalanobis' D as a Multivariate Effect Size

In a previous paper (Del Giudice, 2017 [Heterogeneity coefficients for Mahalanobis' D as a multivariate effect size. Multivariate Behavioral Research, 52, 216–221]), I proposed two heterogeneity coefficients for Mahalanobis' D based on the Gini coefficient, labeled H and EPV. In this addendum I discuss the limitations of the original approach and note that the proposed indices may overestimate heterogeneity under certain conditions. I then describe two revised indices H₂ and EPV₂, and illustrate the difference between the original and revised indices with some real-world data sets.     

The personality of meaning in life: Associations between dimensions of life meaning and the Big Five

The goal of the current study was to identify aspects of personality that are associated with different ways in which people find meaning in life. This was achieved using constrained principal component analysis (CPCA) on data from 322 university students, who completed the Sources of Meaning and Meaning in Life questionnaire and the Big Five Aspects Scale. CPCA demonstrated that personality traits and life meaning are associated, but not redundant, with one another. Specifically, respondents with high scores on lower-level aspects of Openness to Experience tended to derive meaning from questioning, learning and challenging tradition, whereas those with high scores on aspects of Conscientiousness and Extraversion tended to derive meaning from success at work, health, and family. Results suggest that personality traits are associated with variations in the domains used to derive meaning in life, and demonstrate the utility of CPCA as an innovative statistical technique for the study of individual differences.     

A Simple Simulation Technique for Nonnormal Data with Prespecified Skewness,Kurtosis, and Covariance Matrix

We present and investigate a simple way to generate nonnormal data using linear combinations of independent generator (IG) variables. The simulated data have prespecified univariate skewness and kurtosis and a given covariance matrix. In contrast to the widely used Vale-Maurelli (VM) transform, the obtained data are shown to have a non-Gaussian copula. We analytically obtain asymptotic robustness conditions for the IG distribution. We show empirically that popular test statistics in covariance analysis tend to reject true models more often under the IG transform than under the VM transform. This implies that overly optimistic evaluations of estimators and fit statistics in covariance structure analysis may be tempered by including the IG transform for nonnormal data generation. We provide an implementation of the IG transform in the R environment.     

大学新生主观社会地位与抑郁的变化轨迹:一项多元潜变量增长模型分析

采用整群取样的方法在全国按东部、中部和西部共抽取了7所高校的1983名新生,进行了历时四个月的四次追踪测试。使用潜变量增长模型建模,分别考察了新生主观社会地位和抑郁的变化轨迹,并就两者间的变化关系进行了分析。结果发现新生入学后四个月内：（1）主观社会地位呈阶段化线性增长,其起始水平和第一阶段的增长速度存在显著个体间差异;（2）抑郁呈二次方增长,其起始水平和增长速度存在显著个体间差异;（3）入学后主观社会地位的下滑速度能有效预测新生抑郁水平的上升速度。研究基于情绪压制理论,对主观社会地位与抑郁间的变化关系进行了分析。     

Learning Abilities and Disabilities: Generalist Genes, Specialist Environments

ABSTRACT— Twin studies comparing identical and fraternal twins consistently show substantial genetic influence on individual differences in learning abilities such as reading and mathematics, as well as in other cognitive abilities such as spatial ability and memory. Multivariate genetic research has shown that the same set of genes is largely responsible for genetic influence on these diverse cognitive areas. We call these "generalist genes." What differentiates these abilities is largely the environment, especially nonshared environments that make children growing up in the same family different from one another. These multivariate genetic findings of generalist genes and specialist environments have far-reaching implications for diagnosis and treatment of learning disabilities and for understanding the brain mechanisms that mediate these effects.     

The integration of multidimensional scaling and multivariate analysis with optimal transformations

The recent history of multidimensional data analysis suggests two distinct traditions that have developed along quite different lines. In multidimensional scaling (MDS), the available data typically describe the relationships among a set of objects in terms of similarity/dissimilarity (or (pseudo-)distances). In multivariate analysis (MVA), data usually result from observation on a collection of variables over a common set of objects. This paper starts from a very general multidimensional scaling task, defined on distances between objects derived from one or more sets of multivariate data. Particular special cases of the general problem, following familiar notions from MVA, will be discussed that encompass a variety of analysis techniques, including the possible use of optimal variable transformation. Throughout, it will be noted how certain data analysis approaches are equivalent to familiar MVA solutions when particular problem specifications are combined with particular distance approximations.This research was supported by the Royal Netherlands Academy of Arts and Sciences (KNAW). An earlier version of this paper was written during a stay at McGill University in Montréal; this visit was supported by a travel grant from the Netherlands Organization for Scientific Research (NWO). I am grateful to Jim Ramsay and Willem Heiser for their encouragement and helpful suggestions, and to the Editor and referees for their constructive comments.     

Simultaneous classification and multidimensional scaling with external information

For the exploratory analysis of a matrix of proximities or (dis)similarities between objects, one often uses cluster analysis (CA) or multidimensional scaling (MDS). Solutions resulting from such analyses are sometimes interpreted using external information on the objects. Usually the procedures of CA, MDS and using external information are carried out independently and sequentially, although combinations of two of the three procedures (CA and MDS, or multidimensional scaling and using external information) have been proposed in the literature. The present paper offers a procedure that combines all three procedures in one analysis, using a model that describes a partition of objects with cluster centroids represented in a low-dimensional space, which in turn is related to the information in the external variables. A simulation study is carried out to demonstrate that the method works satisfactorily for data with a known underlying structure. Also, to illustrate the method, it is applied to two empirical data sets.     

Gender differences and the Defense Mechanism Test: A comparative study of adolescents in psychiatric care and healthy controls

The aim of this paper was to study gender differences in the percept-genetic method Defense Mechanism Test (DMT) among a group of 60 adolescent subjects. Three subgroups were used: patients with psychotic disorders (Axis 1); borderline personality disorder (Axis 2) according to the DSM-IV classification system, and a non-patient group. The test protocols were scored with respect to 124 DMT variables and analyzed by means of the multivariate projection method Partial Least Squares (PLS) in latent structures. The overall results showed considerable gender differences in the whole group as well as in the subgroups. The most striking finding was that girls compared to boys were characterized by the perceptual defense identification with the opposite sex, which means that they perceived male gestalts instead of females in the stimulus picture. When both gender and diagnostic group membership were considered simultaneously, the influence of diagnostic group membership seemed to be stronger than gender even if there was an interaction between diagnoses and gender. It was concluded that gender must be considered when the DMT is used on adolescent populations.     

Multivariate effects of demand characteristics on the analogue assessment of heterosocial competence

The present study assessed the effects of a contextual demand manipulation (pretreatment assessment versus experiment) and an instructional demand manipulation (high demand for competent behavior versus neutral demand) on the analogue assessment of heterosocial competence. Also assessed was the interaction between each demand manipulation and subject characteristics (high-frequency dating/low heterosocially anxious versus low-frequency dating/high heterosocially anxious college males). Students were assigned randomly to one of four groups receiving differential information and instructions. Students completed a battery of five standard paper-and-pencil questionnaires, responded out loud to 10 taped social situations, and conversed with a female confederate while being videotaped for five minutes. Multivariate analyses revealed highly significant differences between subject groups and between contextual manipulation groups. Univariate analyses revealed that only 3 of the 16 dependent measures, including 2 of the self-reports, were significantly affected by the contextual demand. Results are discussed in light of previous studies of phobic behavior and social skills. Various avenues for future research were presented.